1 research outputs found
Optimal Coverage Control for Swarm Robot Systems using a Mean Field Game
Swarm robot systems, in which many robots cooperate to perform one task, have
attracted a great of attention in recent years. In controlling these systems,
the trade-off between the global optimality and the scalability is a major
challenge. In the present paper, we focus on the mean field game (MFG) as a
possible control method for swarm robot systems. The MFG is a framework to
deduce a macroscopic model for describing robot density profiles from the
microscopic robot dynamics. For a coverage control problem aiming at uniformly
distributing robots over space, we extend the original MFG in order to present
two methods for optimally controlling swarm robots: the model predictive mean
field game (MP-MFG) and the best reply strategy (BRS). Importantly, the MP-MFG
converges to the BRS in the limit of prediction time going to zero, which is
also confirmed by our numerical experiments. In addition, we show numerically
that the optimal input is obtained in both the MP-MFG and the BRS, and widening
the prediction time of the MP-MFG improves the control performance.Comment: This paper has not yet undergone peer review, the findings are
provisional and the conclusions may chang